Abstract
Travel time estimation (TTE) on a specific route is a challenging task since the complex road network structure and hard-captured temporal patterns. Many excellent methods have been proposed to address the aforementioned problems. Some approaches well designed heuristically in a non-learning based way have the advantage of a quick response to the query for travel time estimation. However, these methods are largely affected by the noise of traffic data since they are limited to a single feature. Existing road segment based methods are generally considered intuitive but are not accurate enough for they fail to model complex factors, like delay and direction of intersections. In this paper, we propose a novel attention based sequence learning model for travel time estimation of a path (ASTTE), that not only considers the real-world road network topology as multi-relational data but also refine the problem to the road segment and intersection direction aspects. Besides, we integrate the traffic information as local and neighbor dependency, which helps to monitor dynamic traffic conditions during the trip. The use of the attention mechanism allows the model to focus on significant elements among the path comprises road segments and intersections. High-quality experiments on two real-world datasets have demonstrated the effectiveness and robustness of our framework.
Highlights
Travel time estimation for a given route is a kernel and foundation component of the intelligent transportation system (ITS)
It is widely used in these methods dividing the path into road segments and intersections, if we glance over the picture shown in Fig. 1(a) which depicts a track on the real road network, the overall travel time of a trip given the route can be defined as the summation of consuming time on passing roads and delay time of intersections [9]
All the models in this paper are evaluated by using multiple evaluation metrics in our experiments, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and
Summary
Travel time estimation for a given route is a kernel and foundation component of the intelligent transportation system (ITS). [15] maps the historical GPS trajectory into a higher dimensional feature space from the lower input space and uses a deep neural network (DNN) to capture spatial correlations for travel time estimation These methods are discussed without considering the structure of the urban road network and do not apply to the input is a path without GPS points. Definition 4: (problem statement) Given a trajectory data contains a path sequence l = {ri}, i ∈ [1, N ], |l| = n, the departure time s, and the additional traffic condition information Xτ = (χτ , χτ2 , ..., χτN ) ∈ RN×f1 , where χτi denotes the signal of node i at τ -th time interval in the graph G referred before, f1 represents the dimension space. We apply the attention mechanism combines with meaningful attribute information to integrate and focus on significant elements of the path
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